Improved characterization of brain anisotropy using diffusion MRI
Autor(a) principal: | |
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Data de Publicação: | 2007 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://proa.ua.pt/index.php/revdeti/article/view/17181 |
Resumo: | Second order diffusion tensor analysis of diffusion weighted MR data only accounts for a single intra voxel fibre direction. This poses a problem in many regions of the brain where fibres cross. An anisotropy measurement based on the traditional diffusion tensor model, such as fractional anisotropy (FA), produces significantly low values when there are fibres crossing within the same voxel, or in the presence of other partial volume effects. A new anisotropy index based on the variance of the diffusion MRI signal is described and applied to both simulated and experimental data. A method to normalise this parameter, in order to allow comparisons across scan sessions, is also presented. It is shown that this parameter can characterise white matter in situations in which the diffusion tensor formalism fails to accurately reflect the local diffusion. The images obtained show more detail in the fibre structure, a better contrast between regions of high and low anisotropy, and the main fibre tracts appear to be thicker and brighter, which corresponds better anatomically to the information obtained from structural images. |
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7160 |
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Improved characterization of brain anisotropy using diffusion MRISecond order diffusion tensor analysis of diffusion weighted MR data only accounts for a single intra voxel fibre direction. This poses a problem in many regions of the brain where fibres cross. An anisotropy measurement based on the traditional diffusion tensor model, such as fractional anisotropy (FA), produces significantly low values when there are fibres crossing within the same voxel, or in the presence of other partial volume effects. A new anisotropy index based on the variance of the diffusion MRI signal is described and applied to both simulated and experimental data. A method to normalise this parameter, in order to allow comparisons across scan sessions, is also presented. It is shown that this parameter can characterise white matter in situations in which the diffusion tensor formalism fails to accurately reflect the local diffusion. The images obtained show more detail in the fibre structure, a better contrast between regions of high and low anisotropy, and the main fibre tracts appear to be thicker and brighter, which corresponds better anatomically to the information obtained from structural images.UA Editora2007-01-01T00:00:00Zconference objectconference objectinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://proa.ua.pt/index.php/revdeti/article/view/17181oai:proa.ua.pt:article/17181Eletrónica e Telecomunicações; Vol 4 No 7 (2007); 829-833Eletrónica e Telecomunicações; vol. 4 n.º 7 (2007); 829-8332182-97721645-0493reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttps://proa.ua.pt/index.php/revdeti/article/view/17181https://proa.ua.pt/index.php/revdeti/article/view/17181/12231https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessCorreia, MartaWilliams, GuyHarding, SallyCarpenter, Thomas2022-09-26T11:00:11Zoai:proa.ua.pt:article/17181Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:08:08.000970Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Improved characterization of brain anisotropy using diffusion MRI |
title |
Improved characterization of brain anisotropy using diffusion MRI |
spellingShingle |
Improved characterization of brain anisotropy using diffusion MRI Correia, Marta |
title_short |
Improved characterization of brain anisotropy using diffusion MRI |
title_full |
Improved characterization of brain anisotropy using diffusion MRI |
title_fullStr |
Improved characterization of brain anisotropy using diffusion MRI |
title_full_unstemmed |
Improved characterization of brain anisotropy using diffusion MRI |
title_sort |
Improved characterization of brain anisotropy using diffusion MRI |
author |
Correia, Marta |
author_facet |
Correia, Marta Williams, Guy Harding, Sally Carpenter, Thomas |
author_role |
author |
author2 |
Williams, Guy Harding, Sally Carpenter, Thomas |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Correia, Marta Williams, Guy Harding, Sally Carpenter, Thomas |
description |
Second order diffusion tensor analysis of diffusion weighted MR data only accounts for a single intra voxel fibre direction. This poses a problem in many regions of the brain where fibres cross. An anisotropy measurement based on the traditional diffusion tensor model, such as fractional anisotropy (FA), produces significantly low values when there are fibres crossing within the same voxel, or in the presence of other partial volume effects. A new anisotropy index based on the variance of the diffusion MRI signal is described and applied to both simulated and experimental data. A method to normalise this parameter, in order to allow comparisons across scan sessions, is also presented. It is shown that this parameter can characterise white matter in situations in which the diffusion tensor formalism fails to accurately reflect the local diffusion. The images obtained show more detail in the fibre structure, a better contrast between regions of high and low anisotropy, and the main fibre tracts appear to be thicker and brighter, which corresponds better anatomically to the information obtained from structural images. |
publishDate |
2007 |
dc.date.none.fl_str_mv |
2007-01-01T00:00:00Z |
dc.type.driver.fl_str_mv |
conference object conference object info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://proa.ua.pt/index.php/revdeti/article/view/17181 oai:proa.ua.pt:article/17181 |
url |
https://proa.ua.pt/index.php/revdeti/article/view/17181 |
identifier_str_mv |
oai:proa.ua.pt:article/17181 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://proa.ua.pt/index.php/revdeti/article/view/17181 https://proa.ua.pt/index.php/revdeti/article/view/17181/12231 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
UA Editora |
publisher.none.fl_str_mv |
UA Editora |
dc.source.none.fl_str_mv |
Eletrónica e Telecomunicações; Vol 4 No 7 (2007); 829-833 Eletrónica e Telecomunicações; vol. 4 n.º 7 (2007); 829-833 2182-9772 1645-0493 reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799130539068751872 |